Extracting Explainable Temporal Features in Multivariate Time Series Classification Pipelines
TL;DR
EFFECTS is introduced, a scalable framework for extracting interpretable temporal features that enable model-agnostic explanations of black-box classifiers and also support the construction of inherently interpretable models.
Abstract
Multivariate Time Series Classification (MTSC) is a central task in modern data analytics, with growing impact across domains such as healthcare, finance, and industrial monitoring. As MTSC models are increasingly used in real-world decision-making, the need for explainability has become critical. Existing solutions either rely on feature-extraction frameworks that produce opaque descriptors or on explainable-by-design models tied to specific architectures. We introduce EFFECTS, a scalable framework for extracting interpretable temporal features that enable model-agnostic explanations of black-box classifiers and also support the construction of inherently interpretable models. EFFECTS automatically identifies meaningful time slices and characterizes how they distinguish between classes through intuitive transformations and aggregations. Extensive experiments, including a user study with both novice and experienced data scientists, show that EFFECTS produces clearer and more actionable explanations than architecture-specific methods, while maintaining competitive accuracy and strong runtime performance. The source code and data have been made available at https://github.com/analysis-bots/EFFECTS.